Here, we prepare IMC composites to find excatly these regions on the mIF images.
We will first load the needed libraries and data.
library(cytomapper)
all_images <- loadImages("/Volumes/immucan_volume/processed_data/Panel_1/2022_WORKFLOW/IMC/img/")
panel <- read.csv("/Volumes/immucan_volume/processed_data/Panel_1/2022_WORKFLOW/IMC/panel.csv")
channelNames(all_images) <- panel$name
all_images <- normalize(all_images, inputRange = list(Ecad = c(0,50), CD163 = c(0,5), CD45RA = c(0,10), CD45RO = c(0,50), DNA1 = c(0,100)))
out <- plotPixels(all_images,
colour_by = c("CD163", "Ecad", "CD45RA", "CD45RO", "DNA1"),
colour = list(CD163 = c("black", "red"),
Ecad = c("black", "green"),
CD45RA = c("black", "yellow"),
CD45RO = c("black", "yellow"),
DNA1 = c("black", "blue")),
scale_bar = NULL,
image_title = NULL,
return_images = TRUE)
lapply(names(out$images), function(x){
cur_image <- out$images[[x]]
writeImage(rotate(cur_image, angle = 180), paste0("~/SIB Swiss Institute of Bioinformatics/Robin Liechti - Workflow2021/Zurich_data/IMC_composites/", x, ".tiff"))
})
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sessionInfo()
## R version 4.2.0 (2022-04-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] cytomapper_1.8.0 SingleCellExperiment_1.18.0
## [3] SummarizedExperiment_1.26.1 Biobase_2.56.0
## [5] GenomicRanges_1.48.0 GenomeInfoDb_1.32.1
## [7] IRanges_2.30.0 S4Vectors_0.34.0
## [9] BiocGenerics_0.42.0 MatrixGenerics_1.8.0
## [11] matrixStats_0.62.0 EBImage_4.38.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 RColorBrewer_1.1-3 tools_4.2.0
## [4] bslib_0.3.1 utf8_1.2.2 R6_2.5.1
## [7] svgPanZoom_0.3.4 HDF5Array_1.24.0 vipor_0.4.5
## [10] DBI_1.1.2 colorspace_2.0-3 rhdf5filters_1.8.0
## [13] raster_3.5-15 sp_1.4-7 gridExtra_2.3
## [16] tidyselect_1.1.2 compiler_4.2.0 cli_3.3.0
## [19] DelayedArray_0.22.0 sass_0.4.1 scales_1.2.0
## [22] nnls_1.4 systemfonts_1.0.4 stringr_1.4.0
## [25] digest_0.6.29 tiff_0.1-11 fftwtools_0.9-11
## [28] svglite_2.1.0 rmarkdown_2.14 XVector_0.36.0
## [31] jpeg_0.1-9 pkgconfig_2.0.3 htmltools_0.5.2
## [34] highr_0.9 fastmap_1.1.0 htmlwidgets_1.5.4
## [37] rlang_1.0.2 rstudioapi_0.13 shiny_1.7.1
## [40] jquerylib_0.1.4 generics_0.1.2 jsonlite_1.8.0
## [43] BiocParallel_1.30.0 dplyr_1.0.9 RCurl_1.98-1.6
## [46] magrittr_2.0.3 GenomeInfoDbData_1.2.8 Matrix_1.4-1
## [49] Rcpp_1.0.8.3 ggbeeswarm_0.6.0 munsell_0.5.0
## [52] Rhdf5lib_1.18.0 fansi_1.0.3 viridis_0.6.2
## [55] abind_1.4-5 lifecycle_1.0.1 terra_1.5-21
## [58] stringi_1.7.6 yaml_2.3.5 zlibbioc_1.42.0
## [61] rhdf5_2.40.0 grid_4.2.0 parallel_4.2.0
## [64] promises_1.2.0.1 shinydashboard_0.7.2 crayon_1.5.1
## [67] lattice_0.20-45 locfit_1.5-9.5 knitr_1.39
## [70] pillar_1.7.0 codetools_0.2-18 glue_1.6.2
## [73] evaluate_0.15 png_0.1-7 vctrs_0.4.1
## [76] httpuv_1.6.5 gtable_0.3.0 purrr_0.3.4
## [79] assertthat_0.2.1 ggplot2_3.3.6 xfun_0.31
## [82] mime_0.12 xtable_1.8-4 later_1.3.0
## [85] viridisLite_0.4.0 tibble_3.1.7 beeswarm_0.4.0
## [88] ellipsis_0.3.2